How BFSI Companies Detect Fraud Earlier With Integrated Signal Monitoring

BJ

Bristi Jha

1/1/2026

Average Reading Time: 5 minutes

The BFSI industry is producing an enormous amount of data every second from mobile banking applications right up to banking transactions at the branches and ATMs. Although it has introduced advanced financial services into the industry, it has also raised the risk of fraud.

Detecting fraud is not just a question of recognizing behavior after the event, but of recognizing the signals before they even happen. And this means being able to have a holistic view of what’s going on with a customer across multiple channels.

This means being able to have a holistic view of what's occurring with a customer across multiple channels. Because when the information is fragmented, there’s a chance to have blind spots which fraudsters exploit. Presently, the major Indian BFSI players are opting for the integrated signal monitoring solution, which involves real-time data ingestion, intelligent analytics, and adaptive response.

Here’s how three well-known Indian BFSI brands HDFC Bank, ICICI Bank, and State Bank of India (SBI) are tackling fraud with connected data systems.

HDFC Bank: HDFC Bank: Real-Time Analytics for Digital Payment Fraud

HDFC Bank has to handle millions of digital transactions daily, including UPI, IMPS, NEFT, Card Payment, and Mobile Banking. For such high volumes, conventional rule-based processing cannot handle this workload. To detect fraud earlier, HDFC has unified data streams from mobile apps, branches, ATMs, and payment gateways, real-time scoring engines that analyze transaction patterns, behavioral risk profiles comparing a customer’s current activity with historical behavior, alerts and blocks triggered automatically on suspicious signals.

For instance, if a customer suddenly alters their device location from one city to another in a matter of minutes, and also if multiple high-value transactions happen in a manner that is out of the ordinary, all of these alerts become inputs to analytical models, which then identify potential risk.

HDFC Bank has managed to lower the number of false positives and the time it takes to react to suspicious transactions by centralizing data and using machine learning.

ICICI Bank: Cross-Channel Signal Correlation Reduces Card Fraud

The fraud monitoring approach in ICICI Bank includes cross-channel monitoring of signals. The bank uses the pattern of indicators of fraud in credit card transactions, debit card transactions, ATM withdrawals, as well as internet banking.

However, instead of analyzing the data for each channel separately, ICICI uses event logs, patterns identified with the help of anomaly detection, as well as behavioral, transactional, and device-based risk scoring models.

This integrated view helps the bank detect compromised card usage before large-scale loss occurs. It also gives early warning signals to mobile apps and call centers so they can contact customers immediately.

State Bank of India (SBI): AI-Driven Multi-Signal Surveillance for Digital Fraud

At SBI, fraud monitoring is a gigantic challenge considering the bank's lead in customer base and transaction volume. In order to scale up detection without overwhelming operations, SBI employs unified data pipelines across mobile, online, branches, ATMs, and third-party partners; AI and machine learning models trained on billions of historical events; real-time risk engines; and feedback loops that train the models based on confirmed fraud cases.

SBI is able to recognize unusual patterns of activities, such as low-value transactions conducted rapidly to "test the waters" on the vulnerability of an account, and interfere with automated holds or customer verification.

SBI eliminates customer friction exponentially while improving the accuracy of detection by providing various signals on a single interface.

Learning More on How Integrated Signal Monitoring Works

All these examples viewed above show one trend in common:

  • Unified Data: Relevant signals including digital event logs, data feeds, device data, and behavior data are all unified in one analytical base.
  • Real-Time Risk Assessment: There is no waiting until the next day to receive a report, as it is assessed in real time.
  • Behavioral and Contextual Modeling: Fraud detection is no longer rules based but is now centered on patterns, context, and anomalies within the expected behavior.
  • Automatic and Intelligent Response: The moment a threat is identified, an automatic system can decide to block, alert, or escalate, depending on the level of the threat defined.

How DataSense Connects to These Patterns

The process of detecting fraud in BFSI points to a need to have interconnected, governed, and real-time data platforms. This is what current analytics platforms promise to offer.

A platform such as DataSense enables this shift through:

  • Integrating all the signals, such as the transactions conducted by customers, app logs, device information, and third party data into one place.
  • Helping analysts and risk teams understand what data is available and how reliable the data is.
  • Not only creating reports, but also implementing the findings into systems where they serve as the catalyst for automatic actions.

In other words, DataSense can help BFSI companies towards proactive fraud prevention by turning fragmented signals into actionable intelligence.

Conclusion

Fraud in the digital age moves fast but so can detection when data is connected, monitored, and analyzed in real time.

HDFC Bank, ICICI Bank, and SBI are showing that large volumes of data don’t have to be overwhelming. Signals, when integrated from various channels, facilitate early detection, while the use of analytics in real-time turns threats into opportunities. 

The future of fraud protection is not about writing more rules. It’s about connected data, intelligent monitoring, and taking action before it even reaches to fraud.